Intrusion detection system for SDN network using deep learning approach

机译：使用深度学习方法的SDN网络入侵检测系统

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摘要

Software Defined Network (SDN) is considered as the main component of the next generation network. Security, in this environment, has very challenges and risks. Attacking SDN controller or injecting false flow rules could affect the network and block the entire services. To enhance the SDN network security, we propose an anomaly-based intrusion detection system using deep learning approach. This solution aims to protect the communication channel between the SDN control layer and the SDN infrastructure layer against false data injection attack, and to detect any attempt of attack in SND southbound side. We analyze the flows that circulate in the SDN network, we use the logarithm function followed by the Min/Max scalar technique to normalize the flows features. For the flow classification, we exploit the Relu and Softmax functions. We test the proposed system with CICIDS2017 dataset on an experimental platform combining Mininet environment and ONOS controller. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

机译：软件定义网络（SDN）被认为是下一代网络的主要组件。在这种环境下，安全性面临着巨大的挑战和风险。攻击SDN控制器或注入错误的流规则可能会影响网络并阻止整个服务。为了增强SDN网络的安全性，我们提出了一种使用深度学习方法的基于异常的入侵检测系统。该解决方案旨在保护SDN控制层和SDN基础结构层之间的通信通道免遭错误数据注入攻击，并检测SND南行侧的任何攻击企图。我们分析了在SDN网络中流通的流量，我们使用对数函数，然后使用最小/最大标量技术对流量特征进行归一化。对于流分类，我们利用Relu和Softmax函数。我们在结合Mininet环境和ONOS控制器的实验平台上使用CICIDS2017数据集测试了提出的系统。评估结果证明了所提出的安全解决方案的有效性和效率。

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《International Conference on Theoretical and Applicative Aspects of Computer Science》|2019年|1-6|共6页
会议地点 Skikda(DZ)
作者
Sarra BOUKRIA; Mohamed GUERROUMI;
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作者单位

USTHB University Department of Electronics and Computer Science Algiers Algeria;

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关键词
computer network security; learning (artificial intelligence); pattern classification; software defined networking;

机译：计算机网络安全；学习（人工智能）；模式分类软件定义的网络;

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专利

1. A Comparative Analysis of Deep Learning Approaches for Network Intrusion Detection Systems (N-IDSs): Deep Learning for N-IDSs [J] . Prabaharan Poornachandran, Vinayakumar R, Soman KP International journal of digital crime and forensics . 2019,第3期

机译：网络入侵检测系统（N-IDS）的深度学习方法的比较分析：N-IDS的深度学习
2. Comparison of Machine Learning and Deep Learning Models for Network Intrusion Detection Systems [J] . Niraj Thapa, Zhipeng Liu, Dukka B. KC, Future Internet . 2020,第10期

机译：网络入侵检测系统机器学习与深层学习模型的比较
3. A deep learning approach for effective intrusion detection in wireless networks using CNN [J] . Riyaz B., Ganapathy Sannasi Soft computing: A fusion of foundations, methodologies and applications . 2020,第22期

机译：使用CNN的无线网络有效入侵检测的深度学习方法
4. Intrusion detection system for SDN network using deep learning approach [C] . Sarra BOUKRIA, Mohamed GUERROUMI International Conference on Theoretical and Applicative Aspects of Computer Science . 2019

机译：深度学习方法的SDN网络入侵检测系统
5. Design and Implementation of a Deep Learning based Intrusion Detection System in Software-Defined Networking Environment [D] . Niyaz, Quamar. 2017

机译：软件定义网络环境下基于深度学习的入侵检测系统的设计与实现
6. Explaining the Attributes of a Deep Learning Based Intrusion Detection System for Industrial Control Networks [O] . Zhidong Wang, Yingxu Lai, Zenghui Liu, 2020

机译：解释基于深度学习的工业控制网络入侵检测系统的属性
7. Deep Learning Approach on Network Intrusion Detection System using NSL-KDD Dataset [O] . Sandeep Gurung, Mirnal Kanti Ghose, Aroj Subedi 2019

机译：NSL-KDD DataSet网络入侵检测系统深度学习方法

Intrusion detection system for SDN network using deep learning approach

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